We propose to develop and apply a novel modular agent-based modeling approach for the multilevel study of childhood obesity, with a focus on eating. The research envisioned here is anchored in the brain-to-society system (BtS) approach to the study of eating and obesity prevention in development by a trans-disciplinary team lead by principal investigator Dube, and includes experts from each of the fields most relevant to this proposal. Under the leadership of Co-I Hammond from the Brookings Institution, we propose to build upon agent-based modeling work that is both multi-disciplinary and multi-level, to develop a novel extension of the agent-based methodology-a modular approach that will allow separate consideration of each level of analysis, but importantly will permit straightforward integration of the modules to study multi-level feedbacks and interactions. The modular ABM will examine five levels of influence expected to modulate the complex biology/environment interactions influencing eating behaviors and body weight (BMI): (1) genetics (dopaminergic gene systems DRD2 DRD4); (2) neurobiology (dopamine-striatal and executive control functions); (3) psychological predisposition (restraint/ disinhibition and sensitivity to reward); (4) family (child's attachment style and mother anxiety/depression during pregnancy/early childhood, environmental adversity, food security and poverty); (5) social (mother's social norms and social capital, home food environment). ABM-generated synthetic data will be compared to existing longitudinal empirical data from the MAVAN cohort (lead by Co-I Robert Levitan and Michael Meaney), a sample of mothers and their children observed in a longitudinal within-subject design from the time of pregnancy and birth to examine gene-environment interactions and neurodevelopment. The specific aims of the research proposed here, over the course of the 5- year project, are: (1) to construct separate agent-based modular models for each of five levels of analysis relevant to childhood obesity-genetic, neuro-cognitive, psychological, family, and social/environmental--using early childhood data (0-4 years), (2) to integrate multiple modules of the ABM and explore feedback loops between levels, (3) to test the predictive ability of the modular ABM for the key transitional period of 5-7 years, and (4) to provide further validation of the modular ABM models using functional data analysis. The complete model will enable subsequent exploration of potential policy design of interventions, as well as projection of the implications of identified trends. The modular ABM methodology developed in this project would also be of use for the study of other, similarly complex problems. In brief, then, this project will both develop a novel multilevel methodology (modular agent-based computational modeling) and apply it to the study of childhood obesity to improve our understanding of the multilevel determinants of childhood obesity and help design more effective multilevel interventions that consider the range of biological, family, community, socio-cultural, environmental, policy, and macro-level economic factors that influence diet and physical activity in children. PUBLIC HEALTH RELEVANCE: This project will develop a novel multi-level methodology (modular agent-based computational modeling; ABM) and apply it to the study of childhood obesity to improve our understanding of the multilevel determinants of childhood obesity, with a focus on eating. ABM-generated synthetic data will be compared to existing longitudinal empirical data from a cohort of low socio-economic status mothers and their children observed in a longitudinal within-subject design from the time of pregnancy and birth to examine gene-environment interactions and neurodevelopment. The outcome will help design more effective multilevel interventions that consider the range of biological, family, community, socio-cultural, environmental, policy, and macro-level economic factors that influence diet and body weight in children.